---
license: cc-by-4.0
task_categories:
  - time-series-forecasting
language:
  - en
tags:
  - electricity
  - load-forecasting
  - smart-grid
  - India
  - SLDC
  - energy
  - weather
  - multivariate
  - GRU
  - deep-learning
size_categories:
  - 1K<n<10K
pretty_name: Karnataka SLDC Short-Term Load Forecasting Dataset
configs:
  - config_name: preprocessed
    data_files:
      - split: train
        path: data/preprocessed/Karnataka_SLDC.csv
  - config_name: paper_split
    data_files:
      - split: train
        path: data/paper_split/X_train.csv
      - split: test
        path: data/paper_split/X_test.csv
  - config_name: paper_split_scaled
    data_files:
      - split: train
        path: data/paper_split/X_train_scaled.csv
      - split: test
        path: data/paper_split/X_test_scaled.csv
  - config_name: general_split
    data_files:
      - split: train
        path: data/general_split/X_train.csv
      - split: validation
        path: data/general_split/X_val.csv
      - split: test
        path: data/general_split/X_test.csv
---

# Dataset Card for Karnataka SLDC Short-Term Load Forecasting Dataset

Real-world multivariate time-series dataset collected from the **Karnataka State Load Dispatch Centre (SLDC)**, India (April 22 – August 14, 2024). Combines 15-minute grid-operational telemetry with co-located OpenWeatherMap meteorological observations. Designed for short-term load forecasting (STLF) research with four simultaneous prediction targets: grid frequency, state demand, state generation, and CGS exchange.

---

## Dataset Details

### Dataset Description

Karnataka manages electricity dispatch for ~64 million residents across six distribution companies (DISCOMs). The SLDC publishes grid frequency, state demand/generation, DISCOM-wise consumption, and Central Government Scheme (CGS) net exchange figures at ~15-minute intervals. This dataset merges that telemetry with weather observations and provides ready-to-use train/test/validation splits and 5-fold time-series cross-validation folds.

- **Curated by:** Tapan Kumar Das, IIIT Delhi
- **Language(s):** English
- **License:** CC BY 4.0
- **Collection period:** 2024-04-22 to 2024-08-14
- **Sampling interval:** ~15 minutes
- **Total rows (preprocessed):** 8,034
- **Input features (X):** 18 (7 grid + 10 meteorological + 1 categorical)
- **Target variables (Y):** 4 (current_frequency, state_demand, state_generation, cgs)

### Dataset Sources

- **Repository:** https://huggingface.co/datasets/happyman/Karnataka-SLDC
- **Paper:** Short-Term Load Forecasting for Regional Smart Grid Operations: A Hierarchical Multi-Task Deep Learning Approach Using the Karnataka-SLDC Dataset *(Journal of Supercomputing, under review, 2024)*
- **Grid data source:** Karnataka SLDC real-time portal (public)
- **Weather data source:** OpenWeatherMap Current Weather API (Bangalore, 12.9716°N 77.5946°E)

---

## Uses

### Direct Use

- Short-term load forecasting benchmarks for Indian regional grids
- Multi-target / multi-task time-series regression
- Reproducing the H-MTL-GRU paper experiments (use `paper_split` config with scaled CSVs)
- Evaluating new forecasting architectures (use `general_split` config with 5-fold CV)
- Grid frequency stability analysis

### Out-of-Scope Use

- Long-term (seasonal / annual) load forecasting — the dataset covers only 116 days
- Generalisation to other Indian states without retraining — Karnataka grid characteristics may differ
- Real-time dispatch decision-making without domain expert oversight

---

## Dataset Structure

### Data Fields

**Input Features — X (18 columns + TIME STAMP)**

| Column | Unit | Description |
|---|---|---|
| `ncep_generation` | MW | NCEP / NTPC-Karnataka scheduled power |
| `bescom` | MW | BESCOM zone load |
| `hescom` | MW | HESCOM zone load |
| `cesc` | MW | CESC zone load |
| `gescom` | MW | GESCOM zone load |
| `mescom` | MW | MESCOM zone load |
| `pavagada_kspdcl` | MW | Pavagada solar park output (KSPDCL) |
| `weather_temp` | °C | Ambient temperature |
| `weather_feels_like` | °C | Apparent temperature |
| `weather_temp_min` | °C | Hourly minimum temperature |
| `weather_temp_max` | °C | Hourly maximum temperature |
| `weather_temp_pressure` | hPa | Atmospheric pressure |
| `weather_temp_humidity` | % | Relative humidity |
| `weather_temp_visibility` | m | Visibility (max 10,000 m) |
| `weather_wind_speed` | m/s | Wind speed |
| `weather_wind_deg` | ° | Wind direction |
| `weather_clouds_all` | % | Cloud cover |
| `weather_description` | — | Sky condition string (e.g. "light rain") |

**Target Variables — Y (4 columns + TIME STAMP)**

| Column | Unit | Description |
|---|---|---|
| `current_frequency` | Hz | Grid frequency (nominal 50.0 Hz) |
| `state_demand` | MW | Karnataka total electricity demand |
| `state_generation` | MW | Karnataka total in-state generation |
| `cgs` | MW | CGS net exchange (negative = net export) |

### Data Splits

**Paper Split (80 / 20 chronological)**

| Partition | Rows | Date Range |
|---|---|---|
| Train | 6,427 | 2024-04-22 → 2024-07-30 |
| Test | 1,607 | 2024-07-30 → 2024-08-14 |

**General Split (70 / 10 / 20 chronological)**

| Partition | Rows | Date Range |
|---|---|---|
| Train | 5,623 | 2024-04-22 → 2024-07-24 |
| Val | 803 | 2024-07-24 → 2024-07-30 |
| Test | 1,608 | 2024-07-30 → 2024-08-14 |

**5-Fold Time-Series Cross-Validation** (`TimeSeriesSplit`, expanding window)

| Fold | Train rows | Val rows | Val end date |
|---|---|---|---|
| 1 | 1,339 | 1,339 | 2024-06-27 |
| 2 | 2,678 | 1,339 | 2024-07-04 |
| 3 | 4,017 | 1,339 | 2024-07-22 |
| 4 | 5,356 | 1,339 | 2024-08-02 |
| 5 | 6,695 | 1,339 | 2024-08-14 |

Full per-fold date ranges are in `data/kfold/fold_metadata.json`.

### Normalization (Paper Protocol)

`*_scaled.csv` files use leakage-free scaling fitted on training data only:

- **X numerical (17 cols):** MinMax [0, 1]
- **X categorical:** `weather_description` → one-hot encoded (prefix `wd_`, ~9 binary cols)
- **Y `current_frequency`:** shift by −49.0 Hz
- **Y `state_demand`, `state_generation`, `cgs`:** MinMax [0, 1]

---

## Dataset Creation

### Curation Rationale

No public benchmark exists for Indian state-level SLDC data covering simultaneous frequency, demand, generation, and CGS targets. This dataset fills that gap and enables reproducible evaluation of multi-task deep learning forecasters at the Karnataka SLDC scale.

### Source Data

#### Data Collection and Processing

Grid telemetry was scraped from the Karnataka SLDC public real-time portal at ~15-minute resolution from April 22 to August 14, 2024. Weather observations were retrieved from the OpenWeatherMap Current Weather API at the Bangalore city centroid (12.9716°N, 77.5946°E) at matching timestamps.

The two sources were merged on exact `TIME STAMP`. Four administrative columns (`current_time`, `filled_at`, `weather_temp_sunrise`, `weather_temp_sunset`) were dropped. Rows with any missing value were removed (4 rows), yielding 8,034 clean records.

#### Who are the source data producers?

- **Grid data:** Karnataka State Load Dispatch Centre (KSLDC), Government of Karnataka, India
- **Weather data:** OpenWeatherMap (© OpenWeatherMap Ltd, data sourced from national meteorological services)

### Personal and Sensitive Information

This dataset contains no personal or individually identifiable information. All data describe aggregate grid and environmental measurements at the state level.

---

## Bias, Risks, and Limitations

1. **Short temporal window (116 days):** Only covers April–August 2024. Winter demand peaks, festival-season spikes, and annual seasonal cycles are absent.
2. **Single geographic region:** Karnataka state only. Models trained here may not generalise to other Indian SLDCs.
3. **Single weather station:** Bangalore co-ordinates used for all weather features. Karnataka spans arid, semi-arid, and coastal zones — sub-regional weather variation is not captured.
4. **No holiday or event annotations:** Public holidays, industrial shutdowns, and IPL/cricket events cause demand anomalies that appear as unexplained noise.
5. **CGS sign convention:** Negative CGS values indicate net export (common during Pavagada solar midday surplus). Users should be aware of this accounting convention before modelling.
6. **Data scraper gaps:** A small number of 15-minute slots may be missing due to portal downtime; these appear as absent rows rather than NaN-filled rows.

### Recommendations

- For annual or seasonal forecasting tasks, augment with additional years of SLDC data.
- For multi-zone analysis, collect DISCOM-level weather observations rather than a single city coordinate.
- When comparing results with other papers, confirm whether the 80/20 paper split or the 70/10/20 general split was used.

---

## Usage

```python
# Load directly from the hub — no download required
import pandas as pd

BASE = "https://huggingface.co/datasets/happyman/Karnataka-SLDC/resolve/main"

# Preprocessed full dataset
df = pd.read_csv(f"{BASE}/data/preprocessed/Karnataka_SLDC.csv", parse_dates=["TIME STAMP"])

# Paper split — scaled, ready for deep learning
X_train = pd.read_csv(f"{BASE}/data/paper_split/X_train_scaled.csv", parse_dates=["TIME STAMP"])
X_test  = pd.read_csv(f"{BASE}/data/paper_split/X_test_scaled.csv",  parse_dates=["TIME STAMP"])
Y_train = pd.read_csv(f"{BASE}/data/paper_split/Y_train_scaled.csv", parse_dates=["TIME STAMP"])
Y_test  = pd.read_csv(f"{BASE}/data/paper_split/Y_test_scaled.csv",  parse_dates=["TIME STAMP"])

print(f"Train: {len(X_train)} rows × {len(X_train.columns)-1} features")
print(f"Test : {len(X_test)} rows")
```

```python
# Load as HuggingFace Dataset object
from datasets import load_dataset

ds = load_dataset(
    "happyman/Karnataka-SLDC",
    "paper_split_scaled",   # config name
)
df_train = ds["train"].to_pandas()
```

```python
# 5-fold cross-validation loop
import json, requests, pandas as pd

BASE = "https://huggingface.co/datasets/happyman/Karnataka-SLDC/resolve/main"
meta = requests.get(f"{BASE}/data/kfold/fold_metadata.json").json()

for fold in meta:
    k    = fold["fold"]
    X_tr = pd.read_csv(f"{BASE}/data/kfold/fold_{k}/X_train.csv", parse_dates=["TIME STAMP"])
    Y_tr = pd.read_csv(f"{BASE}/data/kfold/fold_{k}/Y_train.csv", parse_dates=["TIME STAMP"])
    X_va = pd.read_csv(f"{BASE}/data/kfold/fold_{k}/X_val.csv",   parse_dates=["TIME STAMP"])
    Y_va = pd.read_csv(f"{BASE}/data/kfold/fold_{k}/Y_val.csv",   parse_dates=["TIME STAMP"])
    print(f"Fold {k}: train={fold['train_rows']:,}  val={fold['val_rows']:,}")
    # fit your model on X_tr / Y_tr, evaluate on X_va / Y_va
```

```python
# Create lag-window sequences for PyTorch (paper lag = 17)
import numpy as np, pandas as pd, torch
from torch.utils.data import TensorDataset, DataLoader

BASE = "https://huggingface.co/datasets/happyman/Karnataka-SLDC/resolve/main"
LAG  = 17

X = pd.read_csv(f"{BASE}/data/paper_split/X_train_scaled.csv").drop(columns=["TIME STAMP"]).values
Y = pd.read_csv(f"{BASE}/data/paper_split/Y_train_scaled.csv").drop(columns=["TIME STAMP"]).values

def make_windows(X, Y, lag):
    xs = [X[i-lag:i] for i in range(lag, len(X))]
    ys = [Y[i]        for i in range(lag, len(Y))]
    return np.array(xs, dtype=np.float32), np.array(ys, dtype=np.float32)

X_seq, Y_seq = make_windows(X, Y, LAG)
print(f"Sequences: {X_seq.shape}")   # (6410, 17, 25)

loader = DataLoader(
    TensorDataset(torch.from_numpy(X_seq), torch.from_numpy(Y_seq)),
    batch_size=64, shuffle=False,
)
```

---

## Citation

```bibtex
@article{das2024karnataka_stlf,
  title   = {Short-Term Load Forecasting for Regional Smart Grid Operations:
             A Hierarchical Multi-Task Deep Learning Approach
             Using the Karnataka-SLDC Dataset},
  author  = {Das, Tapan Kumar and others},
  journal = {The Journal of Supercomputing},
  year    = {2024},
  note    = {Under review}
}
```

---

## Dataset Card Contact

Contact the corresponding author via the Journal of Supercomputing submission system.